import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.metrics import accuracy_score
import re
from scipy.special import expit # Sigmoid function
np.seterr(divide='ignore', invalid='ignore')
{'divide': 'ignore', 'over': 'warn', 'under': 'ignore', 'invalid': 'ignore'}
data = pd.read_csv('globa_data.csv')
data
| Entity | Year | Access to electricity (% of population) | Access to clean fuels for cooking | Renewable-electricity-generating-capacity-per-capita | Financial flows to developing countries (US $) | Renewable energy share in the total final energy consumption (%) | Electricity from fossil fuels (TWh) | Electricity from nuclear (TWh) | Electricity from renewables (TWh) | ... | Primary energy consumption per capita (kWh/person) | Energy intensity level of primary energy (MJ/$2017 PPP GDP) | Value_co2_emissions_kt_by_country | Renewables (% equivalent primary energy) | gdp_growth | gdp_per_capita | Density\n(P/Km2) | Land Area(Km2) | Latitude | Longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 2000 | 1.613591 | 6.2 | 9.22 | 20000.0 | 44.99 | 0.16 | 0.0 | 0.31 | ... | 302.59482 | 1.64 | 760.000000 | NaN | NaN | NaN | 60 | 652230.0 | 33.939110 | 67.709953 |
| 1 | Afghanistan | 2001 | 4.074574 | 7.2 | 8.86 | 130000.0 | 45.60 | 0.09 | 0.0 | 0.50 | ... | 236.89185 | 1.74 | 730.000000 | NaN | NaN | NaN | 60 | 652230.0 | 33.939110 | 67.709953 |
| 2 | Afghanistan | 2002 | 9.409158 | 8.2 | 8.47 | 3950000.0 | 37.83 | 0.13 | 0.0 | 0.56 | ... | 210.86215 | 1.40 | 1029.999971 | NaN | NaN | 179.426579 | 60 | 652230.0 | 33.939110 | 67.709953 |
| 3 | Afghanistan | 2003 | 14.738506 | 9.5 | 8.09 | 25970000.0 | 36.66 | 0.31 | 0.0 | 0.63 | ... | 229.96822 | 1.40 | 1220.000029 | NaN | 8.832278 | 190.683814 | 60 | 652230.0 | 33.939110 | 67.709953 |
| 4 | Afghanistan | 2004 | 20.064968 | 10.9 | 7.75 | NaN | 44.24 | 0.33 | 0.0 | 0.56 | ... | 204.23125 | 1.20 | 1029.999971 | NaN | 1.414118 | 211.382074 | 60 | 652230.0 | 33.939110 | 67.709953 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3644 | Zimbabwe | 2016 | 42.561730 | 29.8 | 62.88 | 30000.0 | 81.90 | 3.50 | 0.0 | 3.32 | ... | 3227.68020 | 10.00 | 11020.000460 | NaN | 0.755869 | 1464.588957 | 38 | 390757.0 | -19.015438 | 29.154857 |
| 3645 | Zimbabwe | 2017 | 44.178635 | 29.8 | 62.33 | 5570000.0 | 82.46 | 3.05 | 0.0 | 4.30 | ... | 3068.01150 | 9.51 | 10340.000150 | NaN | 4.709492 | 1235.189032 | 38 | 390757.0 | -19.015438 | 29.154857 |
| 3646 | Zimbabwe | 2018 | 45.572647 | 29.9 | 82.53 | 10000.0 | 80.23 | 3.73 | 0.0 | 5.46 | ... | 3441.98580 | 9.83 | 12380.000110 | NaN | 4.824211 | 1254.642265 | 38 | 390757.0 | -19.015438 | 29.154857 |
| 3647 | Zimbabwe | 2019 | 46.781475 | 30.1 | 81.40 | 250000.0 | 81.50 | 3.66 | 0.0 | 4.58 | ... | 3003.65530 | 10.47 | 11760.000230 | NaN | -6.144236 | 1316.740657 | 38 | 390757.0 | -19.015438 | 29.154857 |
| 3648 | Zimbabwe | 2020 | 52.747670 | 30.4 | 80.61 | 30000.0 | 81.90 | 3.40 | 0.0 | 4.19 | ... | 2680.13180 | 10.00 | NaN | NaN | -6.248748 | 1214.509820 | 38 | 390757.0 | -19.015438 | 29.154857 |
3649 rows × 21 columns
# Load the dataset (replace 'your_data.csv' with the actual file name or source)
data = pd.read_csv('globa_data.csv')
# Define a function to clean and convert to float
def clean_and_convert(value):
if isinstance(value, str):
cleaned_value = re.sub(r'[^\d.]', '', value)
if cleaned_value:
return float(cleaned_value)
return value
# Apply the cleaning and conversion function to all columns
for column in data.columns:
data[column] = data[column].apply(clean_and_convert)
# Encode the 'Entity' column using LabelEncoder
# data['Entity'] = label_encoder.fit_transform(data['Entity'])
# Define the target variable and classes
data['Access Level'] = pd.cut(data['Access to electricity (% of population)'],
bins=[0, 20, 40, 60, 80, 100],
labels=['Very Low', 'Low', 'Moderate', 'High', 'Very High'])
data["Access Level"].dropna(inplace=True)
#dropping columns that are not relevant to the analysis
data.drop(columns=['Access to electricity (% of population)'], inplace=True)
data = data.drop(columns=["Renewable energy share in the total final energy consumption (%)"])
data = data.drop(columns=["Renewables (% equivalent primary energy)"])
data = data.drop(columns=["Financial flows to developing countries (US $)"])
data = data.drop(columns=["Renewable-electricity-generating-capacity-per-capita"])
data = data.drop(columns=["Longitude"])
data = data.drop(columns=["Latitude"])
data = data.drop(columns=["Land Area(Km2)"])
isna_mask = data.isna().sum(axis=1) > 0
# print(isna_mask.sum())
data = data[~isna_mask]
y = data["Access Level"].reset_index(drop=True)
# Split the data into features (X) and target (y)
X = data.drop(columns=['Access Level'])
# One-hot encode the categorical 'Entity' column
X = pd.get_dummies(X, columns=['Entity'], prefix=['Entity'])
# Perform feature scaling on the X data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
#train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
label_encoder = LabelEncoder()
label_encoder.fit(y_train)
y_train = label_encoder.transform(y_train)
y_test = label_encoder.transform(y_test)
80/20 Split (Preferred):
When considering the split ratio for my dataset, an 80/20 split stands out as the preferred choice. This allocation reserves 80% of the data for training and sets aside 20% for testing. Here's why it's a strong choice:
Balanced Allocation: The 80/20 split provides an equitable division of the dataset. With 80% for training and 20% for testing, it strikes an optimal balance between effective model learning and comprehensive model evaluation.
Effective Learning: With 80% of the data (2919 samples) dedicated to training, my model can learn from a diverse and representative set of examples. This ample training data allows the model to capture complex patterns and relationships within the dataset, leading to a more robust model.
Thorough Evaluation: The 20% reserved for testing (730 samples) offers a substantial chunk for rigorous model evaluation. This sizeable test set ensures that my model's performance is rigorously assessed, providing meaningful insights into its generalization capability.
Robustness: An 80/20 split is a well-established industry standard, and it is a practical choice for most machine learning tasks. It offers a well-rounded approach to both training and testing, making it an ideal starting point for my classification task.
70/30 Split (Alternative):
While the 70/30 split is a viable alternative, there are some considerations that make the 80/20 split the preferred choice:
Less Test Data: In a 70/30 split, 70% of the data is used for training, leaving 30% for testing. While this still provides a substantial training set, the test set (1094 samples) is slightly smaller. This may limit the thoroughness of the model evaluation.
Balanced Learning and Testing: The 70/30 split slightly tilts the balance toward training, which might be beneficial if training is a primary concern. However, it could compromise the rigor of model testing.
Industry Standard: The 80/20 split is widely adopted in machine learning and data science practices, making it a more familiar and standardized choice. It ensures that our approach aligns with best practices in the field.
In conclusion, while the 70/30 split is certainly reasonable, the 80/20 split offers a more balanced and well-rounded approach to my classification task. It enables my model to learn effectively while maintaining a substantial test set for rigorous evaluation. The 80/20 split is not only a practical choice but also aligns with industry standards, making it the preferred split ratio for my dataset.
X_new = pd.DataFrame(X_scaled, columns = X.columns)
X_new.describe()
| Year | Access to clean fuels for cooking | Electricity from fossil fuels (TWh) | Electricity from nuclear (TWh) | Electricity from renewables (TWh) | Low-carbon electricity (% electricity) | Primary energy consumption per capita (kWh/person) | Energy intensity level of primary energy (MJ/$2017 PPP GDP) | Value_co2_emissions_kt_by_country | gdp_growth | ... | Entity_Uganda | Entity_Ukraine | Entity_United Arab Emirates | Entity_United Kingdom | Entity_United States | Entity_Uruguay | Entity_Uzbekistan | Entity_Vanuatu | Entity_Zambia | Entity_Zimbabwe | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | ... | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 | 2.888000e+03 |
| mean | 1.511380e-14 | -2.952394e-17 | -9.841312e-18 | -2.460328e-17 | 1.476197e-17 | 6.396853e-17 | 4.920656e-17 | 2.165089e-16 | -3.444459e-17 | 8.857181e-17 | ... | -1.476197e-17 | -4.428590e-17 | -3.444459e-17 | -2.460328e-17 | -2.460328e-17 | -7.380984e-17 | -7.380984e-17 | -5.412722e-17 | -5.412722e-17 | -2.460328e-17 |
| std | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | ... | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 | 1.000173e+00 |
| min | -1.670741e+00 | -1.589617e+00 | -2.041295e-01 | -1.940244e-01 | -2.466368e-01 | -1.154582e+00 | -6.996626e-01 | -1.224983e+00 | -2.056651e-01 | -8.755782e+00 | ... | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 |
| 25% | -8.011343e-01 | -1.022023e+00 | -2.033790e-01 | -1.940244e-01 | -2.457977e-01 | -9.742272e-01 | -6.263597e-01 | -5.974468e-01 | -2.029938e-01 | -4.576486e-01 | ... | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 |
| 50% | 6.847250e-02 | 4.749434e-01 | -1.961694e-01 | -1.940244e-01 | -2.303217e-01 | -1.350514e-01 | -3.828887e-01 | -2.864800e-01 | -1.932135e-01 | -2.345189e-02 | ... | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 |
| 75% | 9.380793e-01 | 9.671142e-01 | -1.360866e-01 | -1.940244e-01 | -1.535012e-01 | 8.129803e-01 | 1.537978e-01 | 2.009814e-01 | -1.348503e-01 | 4.442603e-01 | ... | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 | -8.350749e-02 |
| max | 1.633765e+00 | 9.671142e-01 | 1.345998e+01 | 1.011700e+01 | 1.853496e+01 | 1.729051e+00 | 6.397726e+00 | 7.610956e+00 | 1.292949e+01 | 1.284183e+01 | ... | 1.197497e+01 | 1.197497e+01 | 1.197497e+01 | 1.197497e+01 | 1.197497e+01 | 1.197497e+01 | 1.197497e+01 | 1.197497e+01 | 1.197497e+01 | 1.197497e+01 |
8 rows × 161 columns
Columns Removed Due to Missing Values:
In my data preprocessing, I have opted to remove specific columns due to the substantial number of missing values, as these gaps could adversely affect my data analysis. The columns removed, along with their respective counts of missing values, are as follows:
Columns Removed Unrelated to the Study:
In addition to addressing the missing values, I also excluded columns that are not directly relevant to the scope of my study. These columns include:
%%time
# from last time, our logistic regression algorithm is given by (including everything we previously had):
class BinaryLogisticRegression:
def __init__(self, eta, iterations=20, C1=0.001, C2=0.001):
self.eta = eta
self.iters = iterations
self.C1 = C1
self.C2 = C2
# internally we will store the weights as self.w_ to keep with sklearn conventions
def __str__(self):
if(hasattr(self,'w_')):
return 'Binary Logistic Regression Object with coefficients:\n'+ str(self.w_) # is we have trained the object
else:
return 'Untrained Binary Logistic Regression Object'
# convenience, private:
@staticmethod
def _add_bias(X):
return np.hstack((np.ones((X.shape[0],1)),X)) # add bias term
@staticmethod
def _sigmoid(theta):
# increase stability, redefine sigmoid operation
return expit(theta) #1/(1+np.exp(-theta))
# public:
def predict_proba(self,X,add_bias=True):
# add bias term if requested
Xb = self._add_bias(X) if add_bias else X
return self._sigmoid(Xb @ self.w_) # return the probability y=1
def predict(self,X):
return (self.predict_proba(X)>0.5) #return the actual prediction
# vectorized gradient calculation with regularization using L2 Norm
def _get_gradient(self,X,y):
ydiff = y-self.predict_proba(X,add_bias=False).ravel() # get y difference
gradient = np.mean(X * ydiff[:,np.newaxis], axis=0) # make ydiff a column vector and multiply through
gradient = gradient.reshape(self.w_.shape)
#gradient[1:] += -2 * self.w_[1:] * self.C1 # TODO REMOVE THIS
return gradient
def _get_gradient_L2(self, X, y):
gradient = self._get_gradient(X, y)
gradient[1:] += -2 * self.w_[1:] * self.C2
return gradient
def _get_gradient_L1(self, X, y):
gradient = self._get_gradient(X, y)
l1_der = self.w_[1:] / np.abs(self.w_[1:])
l1_der[self.w_[1:] == 0] = 0
gradient[1:] += -1 * l1_der * self.C1
return gradient
def _get_gradient_elastic(self, X, y):
gradient = self._get_gradient(X, y)
l1_der = self.w_[1:] / np.abs(self.w_[1:])
l1_der[self.w_[1:] == 0] = 0
gradient[1:] += -1 * l1_der * self.C1
gradient[1:] += -2 * self.w_[1:] * self.C2
return gradient
def fit(self, X, y, regularization=None):
Xb = self._add_bias(X) # add bias term
num_samples, num_features = Xb.shape
self.w_ = np.zeros((num_features,1)) # init weight vector to zeros
# for as many as the max iterations
for _ in range(self.iters):
if(regularization == 'L1'):
gradient = self._get_gradient_L1(Xb, y)
elif(regularization == 'L2'):
gradient = self._get_gradient_L2(Xb, y)
elif(regularization == 'elastic'):
gradient = self._get_gradient_elastic(Xb, y)
else:
gradient = self._get_gradient(Xb,y)
self.w_ += gradient*self.eta # multiply by learning rate
# add bacause maximizing
blr = BinaryLogisticRegression(eta=0.1,iterations=50,C1=0.001)
blr.fit(X_train,y_train,regularization=None)
yhat = blr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.10553633217993079 CPU times: user 83 ms, sys: 137 ms, total: 220 ms Wall time: 33.2 ms
class StochasticLogisticRegression(BinaryLogisticRegression):
# stochastic gradient calculation
def _get_gradient(self,X,y):
# grab a subset of samples in a mini-batch
# and calculate the gradient according to the small batch only
mini_batch_size = 16
idxs = np.random.choice(len(y), mini_batch_size)
ydiff = y[idxs]-self.predict_proba(X[idxs],add_bias=False).ravel() # get y difference (now scalar)
gradient = np.mean(X[idxs] * ydiff[:,np.newaxis], axis=0) # make ydiff a column vector and multiply through
gradient = gradient.reshape(self.w_.shape)
#gradient[1:] += -2 * self.w_[1:] * self.C2
return gradient
slr = StochasticLogisticRegression(eta=0.01, iterations=300,
C1=0.001,
C2=0.001) # take a lot more steps!!
slr.fit(X_train,y_train,regularization=None)
yhat = slr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.10899653979238755
class VectorBinaryLogisticRegression(BinaryLogisticRegression):
# inherit from our previous class to get same functionality
@staticmethod
def _sigmoid(theta):
# increase stability, redefine sigmoid operation
return expit(theta) #1/(1+np.exp(-theta))
# but overwrite the gradient calculation
def _get_gradient(self,X,y):
ydiff = y-self.predict_proba(X,add_bias=False).ravel() # get y difference
gradient = np.mean(X * ydiff[:,np.newaxis], axis=0) # make ydiff a column vector and multiply through
return gradient.reshape(self.w_.shape)
def _get_gradient_L1(self, X, y):
gradient = self._get_gradient(X, y)
l1_der = self.w_[1:] / np.abs(self.w_[1:])
l1_der[self.w_[1:] == 0] = 0
gradient[1:] += -1 * l1_der * self.C1
return gradient
def _get_gradient_L2(self, X, y):
gradient = self._get_gradient(X, y)
gradient[1:] += -2 * self.w_[1:] * self.C2
return gradient
vlr = VectorBinaryLogisticRegression(eta=0.01, iterations=300,
C1=0.001,
C2=0.001) # take a lot more steps!!
vlr.fit(X_train,y_train,regularization=None)
yhat = vlr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.11072664359861592
blr = BinaryLogisticRegression(eta = 0.001, iterations=100)
blr.fit(X_train, y_train)
y_pred = blr.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
Accuracy: 0.10207612456747404
%%time
# for this, we won't perform our own BFGS implementation
# (it takes a fair amount of code and understanding, which we haven't setup yet)
# luckily for us, scipy has its own BFGS implementation:
from scipy.optimize import fmin_bfgs # maybe the most common bfgs algorithm in the world
from numpy import ma
class BFGSBinaryLogisticRegression(BinaryLogisticRegression):
@staticmethod
def objective_function(w,X,y,C1,C2):
g = expit(X @ w)
# invert this because scipy minimizes, but we derived all formulas for maximzing
return -np.sum(ma.log(g[y==1]))-np.sum(ma.log(1-g[y==0])) + C2*sum(w**2) + C1*sum(np.abs(w))
#-np.sum(y*np.log(g)+(1-y)*np.log(1-g))
@staticmethod
def objective_gradient(w,X,y,C1, C2):
g = expit(X @ w)
ydiff = y-g
gradient = np.mean(X * ydiff[:,np.newaxis], axis=0)
gradient = gradient.reshape(w.shape)
gradient[1:] += -2 * w[1:] * C2
l1_der = w[1:] / np.abs(w[1:])
l1_der[w[1:] == 0] = 0
gradient[1:] += -1 * l1_der * C1
return -gradient
# just overwrite fit function
def fit(self, X, y, regularization=None):
Xb = self._add_bias(X) # add bias term
num_samples, num_features = Xb.shape
if(regularization == 'L1'):
self.C2 = 0
elif(regularization == 'L2'):
self.C1 = 0
elif(regularization == 'elastic'):
pass
else:
self.C1 = 0
self.C2 = 0
self.w_ = fmin_bfgs(self.objective_function, # what to optimize
np.zeros((num_features,1)), # starting point
fprime=self.objective_gradient, # gradient function
args=(Xb,y,self.C1,self.C2), # extra args for gradient and objective function
gtol=1e-03, # stopping criteria for gradient, |v_k|
maxiter=self.iters, # stopping criteria iterations
disp=False)
self.w_ = self.w_.reshape((num_features,1))
bfgslr = BFGSBinaryLogisticRegression(_,iterations=3,C1=1.000,C2=1.000) # note that we need only a few iterations here
bfgslr.fit(X_train,y_train,regularization=None)
yhat = bfgslr.predict(X_test)
#print(bfgslr)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.10034602076124567 CPU times: user 123 ms, sys: 198 ms, total: 321 ms Wall time: 50.1 ms
bfgslr = BFGSBinaryLogisticRegression(_,iterations=3,C1=1.000,C2=1.000)
bfgslr.fit(X_train,y_train,regularization='L1')
yhat = bfgslr.predict(X_test)
#print(bfgslr)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.10034602076124567
bfgslr = BFGSBinaryLogisticRegression(_,iterations=3,C1=1.000,C2=1.000)
bfgslr.fit(X_train,y_train,regularization='L2')
yhat = bfgslr.predict(X_test)
#print(bfgslr)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.10034602076124567
bfgslr = BFGSBinaryLogisticRegression(_,iterations=3,C1=1.000,C2=1.000)
bfgslr.fit(X_train,y_train,regularization='elastic')
yhat = bfgslr.predict(X_test)
#print(bfgslr)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.11245674740484429
%%time
from numpy.linalg import pinv
class HessianBinaryLogisticRegression(BinaryLogisticRegression):
# just overwrite gradient function
def _get_gradient(self,X,y):
g = self.predict_proba(X,add_bias=False).ravel() # get sigmoid value for all classes
hessian = X.T @ np.diag(g*(1-g)) @ X - 2 * self.C2 # calculate the hessian
ydiff = y-g # get y difference
gradient = np.sum(X * ydiff[:,np.newaxis], axis=0) # make ydiff a column vector and multiply through
gradient = gradient.reshape(self.w_.shape)
gradient[1:] += -2 * self.w_[1:] * self.C2
return pinv(hessian) @ gradient
hlr = HessianBinaryLogisticRegression(eta=1.0,
iterations=4,
C1=0.001,
C2=0.001) # note that we need only a few iterations here
hlr.fit(X_train,y_train, regularization='elastic')
yhat = hlr.predict(X_test)
#print(hlr)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.09169550173010381 CPU times: user 824 ms, sys: 476 ms, total: 1.3 s Wall time: 178 ms
# allow for the user to specify the algorithm they want to solver the binary case
class MultiClassLogisticRegression:
def __init__(self, eta, iterations=20,
C1=0.0001,
C2=0.0001,
solver=BFGSBinaryLogisticRegression):
self.eta = eta
self.iters = iterations
self.C1 = C1
self.C2 = C2
self.solver = solver
self.classifiers_ = []
# internally we will store the weights as self.w_ to keep with sklearn conventions
def __str__(self):
if(hasattr(self,'w_')):
return 'MultiClass Logistic Regression Object with coefficients:\n'+ str(self.w_) # is we have trained the object
else:
return 'Untrained MultiClass Logistic Regression Object'
def fit(self,X,y, regularization=None):
num_samples, num_features = X.shape
self.unique_ = np.sort(np.unique(y)) # get each unique class value
num_unique_classes = len(self.unique_)
self.classifiers_ = []
for i,yval in enumerate(self.unique_): # for each unique value
y_binary = np.array(y==yval).astype(int) # create a binary problem
# train the binary classifier for this class
hblr = self.solver(eta=self.eta,iterations=self.iters,
C1=self.C1,
C2=self.C2
)
hblr.fit(X,y_binary, regularization=regularization)
# add the trained classifier to the list
self.classifiers_.append(hblr)
# save all the weights into one matrix, separate column for each class
self.w_ = np.hstack([x.w_ for x in self.classifiers_]).T
def predict_proba(self,X):
probs = []
for hblr in self.classifiers_:
probs.append(hblr.predict_proba(X).reshape((len(X),1))) # get probability for each classifier
return np.hstack(probs) # make into single matrix
def predict(self,X):
return self.unique_[np.argmax(self.predict_proba(X),axis=1)] # take argmax along row
%%time
lr = MultiClassLogisticRegression(eta=1.0,
iterations=4,
C1=0.01,
C2=0.01,
solver=BFGSBinaryLogisticRegression
)
lr.fit(X_train,y_train)
#print(lr)
yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.870242214532872 CPU times: user 109 ms, sys: 157 ms, total: 265 ms Wall time: 51.9 ms
%%time
lr = MultiClassLogisticRegression(eta=1.0,
iterations=4,
C1=0.01,
C2=0.01,
solver=HessianBinaryLogisticRegression
)
lr.fit(X_train,y_train,regularization=None)
#print(lr)
yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.9204152249134948 CPU times: user 4.13 s, sys: 2.53 s, total: 6.66 s Wall time: 917 ms
%%time
lr = MultiClassLogisticRegression(eta=0.1,
iterations=100,
C1=0.01,
C2=0.01,
solver=StochasticLogisticRegression
)
lr.fit(X_train,y_train,regularization="L1")
#print(lr)
yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.8823529411764706 CPU times: user 64.1 ms, sys: 9.8 ms, total: 73.9 ms Wall time: 66.8 ms
%%time
lr = MultiClassLogisticRegression(eta=0.1,
iterations=4,
C1=0.01,
C2=0.01,
solver=VectorBinaryLogisticRegression
)
lr.fit(X_train,y_train,regularization=None)
#print(lr)
yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.8806228373702422 CPU times: user 50.9 ms, sys: 85.9 ms, total: 137 ms Wall time: 32.6 ms
param = {
'iterations': [10, 100, 1000],
'C1': [0.1, 0.01, 0.001, 0.0001],
'C2': [0.1, 0.01, 0.001, 0.0001],
'regularization': [None, "L1", "L2", "elastic"],
'solver': [
("Quasi-Newton", BFGSBinaryLogisticRegression), # Quasi-Newton's method
("SGD", StochasticLogisticRegression),
("Gradient", VectorBinaryLogisticRegression) # Gradient Descent
],
}
accuracies = []
param_accuracies = []
for (sn, s) in param['solver']:
for c1 in param['C1']:
for c2 in param['C2']:
for r in param['regularization']:
for it in param['iterations']:
#%%time
params = f"solver: {sn} Regularization: {r} C1: {c1} C2: {c2} iters: {it}"
print(params)
lr = MultiClassLogisticRegression(eta=0.1,
iterations=it,
C1=c1,
C2=c2,
solver=s
)
lr.fit(X_train,y_train)
#print(lr)
yhat = lr.predict(X_test)
#print('Accuracy of: ',accuracy_score(y_test,yhat))
acc = accuracy_score(y_test, yhat)
accuracies.append(acc)
param_accuracies.append(params)
print('Accuracy: ', acc, params)
accuracies = np.array(accuracies)
param_accuracies = np.array(param_accuracies)
ind = np.argpartition(accuracies, -5)[-5:]
top5acc = accuracies[ind]
top5params = param_accuracies[ind]
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000 solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.7439446366782007 solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 10 solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.8719723183391004 solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 100 solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.8944636678200693 solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 1000 solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.7975778546712803 solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 10 solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.8771626297577855 solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 100 solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.884083044982699 solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000 solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.7370242214532872 solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 10 solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.8581314878892734 solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 100 solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.8944636678200693 solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000 solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.745674740484429 solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 10 solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 100 solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.884083044982699 solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000 solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.7785467128027682 solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 10 solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.8719723183391004 solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 100 solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.8996539792387543 solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 1000 solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.7698961937716263 solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 10 solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.8598615916955017 solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 100 solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.8961937716262975 solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000 solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.7889273356401384 solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 10 solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.8615916955017301 solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 100 solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.8823529411764706 solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000 solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.7422145328719724 solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 10 solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.8494809688581315 solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 100 solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.889273356401384 solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000 solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.7820069204152249 solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 10 solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.8650519031141869 solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 100 solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.889273356401384 solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 1000 solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.7370242214532872 solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 10 solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.856401384083045 solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 100 solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.9013840830449827 solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000 solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.759515570934256 solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 10 solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.8633217993079585 solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 100 solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.8927335640138409 solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000 solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.773356401384083 solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 10 solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.8598615916955017 solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 100 solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.8910034602076125 solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000 solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.7647058823529411 solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 10 solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.8512110726643599 solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 100 solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.870242214532872 solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 1000 solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.7750865051903114 solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10 solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.8615916955017301 solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100 solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.8806228373702422 solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000 solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.7525951557093425 solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10 solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.8442906574394463 solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100 solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.8961937716262975 solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000 solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.7647058823529411 solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10 solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.8615916955017301 solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100 solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.8771626297577855 solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000 solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.7560553633217993 solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 10 solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.8633217993079585 solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 100 solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.8927335640138409 solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 1000 solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.7698961937716263 solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 10 solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.8771626297577855 solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 100 solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.884083044982699 solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000 solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.745674740484429 solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 10 solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.8667820069204152 solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 100 solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.8927335640138409 solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000 solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.7820069204152249 solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 10 solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.8598615916955017 solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 100 solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.8788927335640139 solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000 solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.7785467128027682 solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 10 solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.8650519031141869 solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 100 solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.8788927335640139 solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 1000 solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.759515570934256 solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 10 solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 100 solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.8858131487889274 solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000 solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.745674740484429 solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 10 solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.8581314878892734 solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 100 solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.8910034602076125 solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000 solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.7577854671280276 solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 10 solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.8737024221453287 solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 100 solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.8754325259515571 solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000 solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.71280276816609 solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 10 solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.8719723183391004 solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 100 solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.8823529411764706 solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 1000 solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.7577854671280276 solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 10 solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.8685121107266436 solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 100 solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.889273356401384 solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000 solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.7716262975778547 solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 10 solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.8546712802768166 solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 100 solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.8788927335640139 solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000 solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.7162629757785467 solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 10 solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.8685121107266436 solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 100 solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.8858131487889274 solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000 solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.7802768166089965 solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 10 solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.8615916955017301 solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 100 solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.8875432525951558 solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 1000 solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.7716262975778547 solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10 solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100 solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.8858131487889274 solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000 solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.7923875432525952 solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10 solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.8581314878892734 solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100 solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.8806228373702422 solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000 solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.7889273356401384 solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10 solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.8650519031141869 solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100 solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.8754325259515571 solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000 solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.7768166089965398 solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 10 solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.8667820069204152 solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 100 solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.8771626297577855 solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 1000 solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.7629757785467128 solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 10 solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 100 solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.889273356401384 solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000 solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.7629757785467128 solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 10 solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.8685121107266436 solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 100 solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.8875432525951558 solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000 solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.7785467128027682 solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 10 solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.8546712802768166 solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 100 solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.8961937716262975 solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000 solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.745674740484429 solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 10 solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.856401384083045 solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 100 solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.8961937716262975 solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 1000 solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.7612456747404844 solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 10 solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.8719723183391004 solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 100 solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.884083044982699 solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000 solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.7854671280276817 solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 10 solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.8667820069204152 solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 100 solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.8875432525951558 solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000 solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.7820069204152249 solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 10 solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.8546712802768166 solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 100 solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.889273356401384 solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000 solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.7854671280276817 solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 10 solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.8615916955017301 solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 100 solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.889273356401384 solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 1000 solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.7370242214532872 solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 10 solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.8667820069204152 solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 100 solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.884083044982699 solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000 solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.7664359861591695 solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 10 solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.8667820069204152 solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 100 solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.884083044982699 solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000 solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.7802768166089965 solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 10 solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.8615916955017301 solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 100 solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.8823529411764706 solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000 solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.7577854671280276 solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 10 solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.8650519031141869 solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 100 solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.8823529411764706 solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 1000 solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.7577854671280276 solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10 solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.8650519031141869 solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100 solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.889273356401384 solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000 solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.7525951557093425 solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10 solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.8633217993079585 solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100 solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.8806228373702422 solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000 solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.7923875432525952 solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10 solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.8771626297577855 solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100 solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.8910034602076125 solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000 solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.7577854671280276 solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 10 solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.8529411764705882 solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 100 solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.8788927335640139 solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 1000 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.7629757785467128 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.8442906574394463 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.9013840830449827 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.7664359861591695 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.8650519031141869 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.8910034602076125 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.7387543252595156 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.8719723183391004 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.8875432525951558 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000 solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.7768166089965398 solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 10 solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.8512110726643599 solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 100 solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.8858131487889274 solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 1000 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.7820069204152249 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.8771626297577855 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.903114186851211 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.7785467128027682 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.8685121107266436 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.8944636678200693 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.7647058823529411 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.8581314878892734 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.8806228373702422 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000 solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.7577854671280276 solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 10 solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.8615916955017301 solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 100 solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.8875432525951558 solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 1000 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.7560553633217993 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.8685121107266436 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.8823529411764706 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.7629757785467128 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.8615916955017301 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.8823529411764706 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.7854671280276817 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.8771626297577855 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.8944636678200693 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000 solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.7629757785467128 solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 10 solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.8442906574394463 solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 100 solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.8944636678200693 solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.773356401384083 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.8529411764705882 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.884083044982699 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.7197231833910035 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.8685121107266436 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.889273356401384 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.773356401384083 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.8598615916955017 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.8961937716262975 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000 solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 10 solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 100 solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 1000 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 10 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 100 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 10 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 100 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 10 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 100 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000 solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 10 solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 100 solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 1000 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 10 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 100 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 10 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 100 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 10 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 100 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000 solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 10 solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 100 solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 1000 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 10 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 100 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 10 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 100 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 10 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 100 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000 solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 10 solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 100 solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 1000 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000 solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 10 solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 100 solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 1000 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 10 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 100 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 10 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 100 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 10 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 100 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000 solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 10 solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 100 solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 1000 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 10 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 100 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 10 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 100 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 10 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 100 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000 solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 10 solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 100 solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 1000 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 10 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 100 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 10 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 100 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 10 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 100 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000 solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 10 solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 100 solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 1000 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000 solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 10 solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 100 solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 1000 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 10 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 100 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 10 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 100 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 10 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 100 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000 solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 10 solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 100 solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 1000 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 10 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 100 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 10 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 100 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 10 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 100 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000 solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 10 solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 100 solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 1000 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 10 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 100 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 10 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 100 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 10 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 100 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000 solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 10 solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 100 solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 1000 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000 solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 10 solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 100 solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 1000 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000 solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 10 solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 100 solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 1000 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000 solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 10 solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 100 solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 1000 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000 solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 10 solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 100 solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.884083044982699 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100 Accuracy: 0.870242214532872 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000 Accuracy: 0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000
print(top5acc)
print(top5params)
[0.93598616 0.93598616 0.93598616 0.93598616 0.93598616] ['solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100' 'solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000' 'solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000' 'solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100' 'solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 100']
param = {
'iterations': [1, 4, 10],
'C1': [0.1, 0.01, 0.001, 0.0001],
'C2': [0.1, 0.01, 0.001, 0.0001],
'regularization': [None, "L1", "L2", "elastic"],
'solver': [
("Newton", HessianBinaryLogisticRegression), # Newton's method
],
}
accuracies = []
param_accuracies = []
for (sn, s) in param['solver']:
for c1 in param['C1']:
for c2 in param['C2']:
for r in param['regularization']:
for it in param['iterations']:
#%%time
params = f"solver: {sn} Regularization: {r} C1: {c1} C2: {c2} iters: {it}"
print(params)
lr = MultiClassLogisticRegression(eta=0.1,
iterations=it,
C1=c1,
C2=c2,
solver=s
)
lr.fit(X_train,y_train)
#print(lr)
yhat = lr.predict(X_test)
#print('Accuracy of: ',accuracy_score(y_test,yhat))
acc = accuracy_score(y_test, yhat)
accuracies.append(acc)
param_accuracies.append(params)
print('Accuracy: ', acc, params)
accuracies = np.array(accuracies)
param_accuracies = np.array(param_accuracies)
ind = np.argpartition(accuracies, -5)[-5:]
top5acc = accuracies[ind]
top5params = param_accuracies[ind]
solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 1 solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 4 solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 10 solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 1 solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 4 solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 10 solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 1 solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 4 solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 10 solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 1 solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 4 solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 10 solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 1 solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 4 solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 10 solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 1 solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 4 solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 10 solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 1 solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 4 solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 10 solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 1 solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 4 solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 10 solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 1 solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 4 solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 10 solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 1 solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 4 solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 10 solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 1 solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 4 solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 10 solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 1 solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 4 solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 10 solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 1 solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 4 solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 10 solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1 solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 4 solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10 solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1 solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 4 solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10 solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1 solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 4 solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10 solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 1 solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 4 solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 10 solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 1 solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 4 solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 10 solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 1 solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 4 solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 10 solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 1 solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 4 solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 10 solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 1 solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 4 solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 10 solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 1 solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 4 solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 10 solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 1 solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 4 solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 10 solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 1 solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 4 solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 10 solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 1 solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 4 solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 10 solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 1 solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 4 solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 10 solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 1 solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 4 solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 10 solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 1 solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 4 solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 10 solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 1 solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 4 solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 10 solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1 solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 4 solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10 solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1 solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 4 solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10 solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1 solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 4 solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10 solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 1 solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 4 solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10 solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 1 solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 4 solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10 solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 1 solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 4 solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10 solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 1 solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 4 solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 10 solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 1 solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 4 solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 10 solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 1 solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 4 solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 10 solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 1 solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 4 solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 10 solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 1 solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 4 solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 10 solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 1 solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 4 solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 10 solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 1 solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 4 solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 10 solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 1 solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 4 solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 10 solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 1 solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 4 solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 10 solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 1 solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 4 solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 10 solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1 solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 4 solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10 solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1 solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 4 solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10 solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1 solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 4 solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10 solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 1 solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 4 solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 10 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 4 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 4 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 4 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10 solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 1 solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 4 solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 10 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 4 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 4 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 4 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10 solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 1 solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 4 solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 10 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 4 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 4 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 4 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10 solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 1 solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 4 solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 10 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 4 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 4 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1 Accuracy: 0.8719723183391004 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 4 Accuracy: 0.8737024221453287 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 4 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10 Accuracy: 0.8806228373702422 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10
print(top5acc)
print(top5params)
[0.88062284 0.88062284 0.88062284 0.88062284 0.88062284] ['solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10' 'solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10' 'solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10' 'solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10' 'solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10']
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly
import plotly.io as pio
iter = [2, 4, 8, 16 ,32, 128, 256, 512, 1024]
iter_accur = []
for i in iter:
lr = MultiClassLogisticRegression(eta=0.1,
iterations= i,
C1=0.01,
C2=0.01,
solver=BFGSBinaryLogisticRegression
)
lr.fit(X_train,y_train)
y_hat_new = lr.predict(X_test)
iter_accur.append(accuracy_score(y_test, y_hat_new))
# using plotly
fig = go.Figure(data=go.Scatter(x=iter, y=iter_accur))
fig.update_layout(title_text='Quasi Newton - Accuracy Vs Number of Iterations Quasi Newton', xaxis_title="Number of Iterations", yaxis_title="Accuracy")
fig.show()
#print(lr)
The depicted graph reveals a noteworthy trend: after 128 iterations, the accuracy levels off, indicating a plateau in model improvement. Consequently, it can be inferred that the optimal number of iterations should be any value greater than or equal to 128. This observation aligns with my understanding of the iterative optimization process, where a new gradient is computed in each iteration, a process influenced by the learning rate.
In the specific context of my analysis, employing a learning rate of 0.1 results in the model converging to an accuracy of 93 percent at the 128th iteration. This convergence point signifies that, at this learning rate, the model reaches a stable and satisfactory accuracy level within 128 iterations, underlining the importance of an appropriate learning rate in the optimization process.
c1 = [.0001, .001, .01, .1, 1]
c2 = [.0001, .001, .01, .1, 1]
C_accur = []
for i, j in zip (c1, c2):
lr = MultiClassLogisticRegression(eta=0.01,
iterations=10,
C1= i,
C2= j ,
solver=BFGSBinaryLogisticRegression
)
lr.fit(X_train,y_train, regularization = "elastic")
y_hat_new = lr.predict(X_test)
C_accur.append(accuracy_score(y_test, y_hat_new))
# using plotly
fig = go.Figure(data=go.Scatter(x=c1, y=C_accur))
fig.update_layout(title_text='Quasi Newton - Accuracy Vs Regularization Strength', xaxis_title="Regularization Strength (ElasticNet)", yaxis_title="Accuracy")
fig.show()
import time
optimizations = [
BFGSBinaryLogisticRegression, # Quasi-Newton's method
StochasticLogisticRegression,
VectorBinaryLogisticRegression,
HessianBinaryLogisticRegression
# Gradient Descent
]
accur= []
times = []
for i in optimizations:
print(i)
model = MultiClassLogisticRegression(eta=0.1,
iterations= 10,
C1=0,
C2=0,
solver= i
)
start = time.time()
model.fit(X_train, y_train)
end = time.time()
y_hat_new = model.predict(X_test)
accur.append(accuracy_score(y_test, y_hat_new))
times.append(end-start)
print(end-start)
<class '__main__.BFGSBinaryLogisticRegression'> 0.10863828659057617 <class '__main__.StochasticLogisticRegression'> 0.0032608509063720703 <class '__main__.VectorBinaryLogisticRegression'> 0.022936105728149414 <class '__main__.HessianBinaryLogisticRegression'> 2.4437451362609863
print(accur)
print(times)
[0.903114186851211, 0.7854671280276817, 0.884083044982699, 0.8806228373702422] [0.10863828659057617, 0.0032608509063720703, 0.022936105728149414, 2.4437451362609863]
%%time
lr = MultiClassLogisticRegression(eta=0.01,
iterations=1000,
C1=0.01,
C2=0.01,
solver=BFGSBinaryLogisticRegression
)
lr.fit(X_train,y_train)
#print(lr)
yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))
Accuracy of: 0.9359861591695502 CPU times: user 1.4 s, sys: 1.54 s, total: 2.94 s Wall time: 424 ms
%%time
from sklearn.linear_model import LogisticRegression as SKLogisticRegression
sci_kit_model = SKLogisticRegression(solver='lbfgs', max_iter=1000, multi_class='ovr', penalty = "none")
sci_kit_model.fit(X_train, y_train)
print(accuracy_score(y_true=y_test, y_pred=sci_kit_model.predict(X_test)))
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
0.9377162629757786 CPU times: user 5.78 s, sys: 5.89 s, total: 11.7 s Wall time: 1.62 s
%%time
from sklearn.linear_model import LogisticRegression as SKLogisticRegression
sci_kit_model = SKLogisticRegression(solver='lbfgs', max_iter=1000, penalty = "none")
sci_kit_model.fit(X_train, y_train)
print(accuracy_score(y_true=y_test, y_pred=sci_kit_model.predict(X_test)))
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
0.9653979238754326 CPU times: user 2.96 s, sys: 3.32 s, total: 6.27 s Wall time: 863 ms
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
Comparing Sklearn and Custom BFGS Implementation:
In my evaluation, I compared the Sklearn optimizer and our custom BFGS implementation. Here are the key findings:
To evaluate which implementation would be more suitable, there would be a trade-off to take into account between time and accuracy. Or implementation presents an accuracy that is 3 percent lower than that of Sklearn. Moreover, the time it takes is double that of the Sklearn implementation. In terms of cost, I would suggest picking SKlearn's implementation as it has a higher accuracy, which would be more suitable for providing a viable model. I must also consider that Sklearn takes half of the time that it takes for our implementation. This is even more noteworthy if dealing with large datasets, making Sklearn a more favorable option in scenarios where efficiency is critical factor.
Differences Between Parameters:
In my experimentation, I implemented the standard BFGS optimization method, whereas Sklearn employs the lightweight BFGS variant. Sklearn's approach incorporates a convergence criterion based on a maximum of 100 iterations, allowing for early termination if accuracy plateaus. This divergence in methodology may account for the faster execution time observed in Sklearn compared to our custom implementation, as it effectively avoids redundant computations.
import time
iter = [2, 4, 8, 16 ,32, 128, 256, 512, 1024]
iter_accur = []
sci_kit_accur = []
time_BFGS = []
time_SKLEARN = []
for i in iter:
lr = MultiClassLogisticRegression(eta=0.1,
iterations= i,
C1=0.01,
C2=0.01,
solver=BFGSBinaryLogisticRegression
)
sci_kit_model = SKLogisticRegression(solver='lbfgs', max_iter=i, penalty = "none")
start1 = time.time()
sci_kit_model.fit(X_train, y_train)
end1 = time.time()
start2 =time.time()
lr.fit(X_train,y_train)
end2 = time.time()
y_hat_new = lr.predict(X_test)
y_hat_sk = sci_kit_model.predict(X_test)
iter_accur.append(accuracy_score(y_test, y_hat_new))
time_BFGS.append(end2- start2)
sci_kit_accur.append(accuracy_score(y_test, y_hat_sk))
time_SKLEARN.append(end1- start1)
# using plotly
# trace1 = go.Scatter(
# x = iter,
# y = iter_accur
# )
# trace2 = go.Scatter(
# x = iter,
# y = sci_kit_accur
# )
# data = [trace1, trace2]
# fig = go.Figure(data=data )
# fig.update_layout(title_text='Accuracy Vs Number of Iterations Quasi Newton', xaxis_title="Number of Iterations", yaxis_title="Accuracy")
# fig.show()
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:
`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
trace1 = go.Scatter(
x = np.cumsum(time_BFGS),
y = iter_accur
)
data = [trace1]
fig = go.Figure(data=data )
fig.update_layout(title_text='Custom Implementation Accuracy Vs Time', xaxis_title="Time (seconds)", yaxis_title="Accuracy")
fig.show()
trace2 = go.Scatter(
x = np.cumsum(time_SKLEARN),
y = sci_kit_accur
)
data = [trace2]
fig = go.Figure(data=data )
fig.update_layout(title_text='SKLearn Accuracy Vs Time', xaxis_title="Time (seconds)", yaxis_title="Accuracy")
fig.show()
In terms of deploying a suitable implementation, it would be preferred SKLearn's model. The accuracy and runtime of their model is more accurate and faster than my custom implementation. For the objective of meeting the business case, my models didn't quite reach the desired accuracy level. The business case ideally calls for an accuracy above 95 percent, with 7 percent margin for error, which is considered quite good for many real-world applications. Our best accuracy was 93.5%, indicating that we were able to classify the majority of the population with access to electricity.
When it comes to deploying a model for practical use, SKlearn's model out performs my custom implementation with an accuracy of 0.963. SKLearn is known for its efficiency and versatility, show casing a better accuracy and runtime performance, making it a more attractive option for real-world applications. In contrast, our accuracy, while commendable, doesn't quite match the precision achieved by SKlearn's model. The flexibility and regularization inherent to SKLearn is advantageous in various contexts demonstrating how it outperforms our model with a higher accuracy and a faster run time.
My experimentation led me to identify BFGS as the most efficient solver, achieving the highest accuracy for our personalized setup of 93.5%. Interestingly, although my custom BFGS model takes significantly longer than SKlearn, it still delivers acceptable accuracy.
In my detailed analysis of BFGS, I observed that boosting the number of iterations also elevates the accuracy. Interestingly, in my tests, accuracy seemed to plateau when hitting 128 iterations. Additionally, I discovered that increases in regularization strength inversely impacts BFGS accuracy.
my implementation delivered a slightly worse accuracy in a less time-efficient manor than SKlearn's LBFGS implementation.
While my model offers certain benefits, it falters in satisfying corporate standards for accuracy and falls short in efficiency. Efficiency is especially problematic when dealing with vast data quantities, implying that organizations handling extensive datasets would witness my model consuming over double the time taken by SKlearn, and sadly, delivering a lower accuracy to boot.
However, considering SKlearn's impressive 96% accuracy, it qualifies as a viable choice for practical applications, given its robust accuracy and time-efficient operation. For example, an energy firms could potentially use our SKlearn-based model to assess electricity availability across various global regions. A solid accuracy rate of 96.3% not only meets, but surpasses our business requirements, highlighting the model's ability to succeed as an application in real-world scenarios.
I was curious what elements impact the accuracy of the VectorBinaryLogisticRegression because I noticed it was performing quite similar to our implementation of BFGS.
I decided to test eta values from .01 to .09 and C values like .001, .005, .015, .1, and .15. By testing these I could see if any had a role in improving accuracy.
I also wanted to see if up to 150 iterations for penalties and C values could also impact the accuracy. I found a slighty higher accuracy of .884. This occurs many times whenever there is no penalty, C is very low either .005 or .001, and eta is around .1 or .2. Interestly, the number of iterations seemed to have a small effect. When I hit higher iterations, etas, C values, the accuracy dropped. I think this is likely because overfitting/overstepping and over regularization come into play.
All in all, it was a pretty insightful experiment, and I learned a lot about how different parameters can affect the accuracy of our models. It's fascinating how something as small as tweaking C or eta values or the number of iterations can impact the results.
from itertools import product
etas = range(1, 10, 1)
cs = [0.001, 0.005, 0.015, 0.1, .15]
iterations = [25, 50,75,100,125, 150]
penalties = ['none', 'L1', 'L2', 'L1L2']
for penalty, c, itrs, eta in product(penalties, cs, iterations, etas):
lr = MultiClassLogisticRegression(eta=eta/100,
iterations= itrs,
C1=c,
C2=c,
solver=VectorBinaryLogisticRegression
)
lr.fit(X_train, y_train)
yhat = lr.predict(X_test)
print(f'When Penalty: {penalty} C: {c}, with Eta: {eta/100}, for Stochastic Logistic Regression, where iters: {itrs} Accuracy of: {accuracy_score(y_test, yhat)}')
When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004 When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706 When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699 When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422 When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855 When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139 When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287 When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436 When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872 When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004